On-Device Fine-Tuning and Domain Adaption: Models in the wild need to be fine-tuned periodically to account for shifts in incoming data distribution [1]} to be robust across domains. Firstly, while several on-device learning frameworks have been proposed for edge devices [2]}, they either work on high-end edge devices (e.g., Raspberry Pi) [3]}[4]} or can update weights of a few layers on microcontrollers [1]}.
Software-centric resource constraints, constrained learning theories, and static resource budget prevent on-device learning from being a viable alternative to cloud-based training for microcontrollers [2]}. Secondly, an alternate line of work suggests low-latency compressive offloading onto the cloud [7]}, but have non-deterministic compression ratios and offloading points. Lastly, the models themselves can be made more robust to domain shifts through representation learning [8]} or domain-adversarial training [9]}, but the resulting models do not fit on microcontrollers. More work needs to be done in striking an optimal balance between on-device fine-tuning and over-the-air model updates, and whether unsupervised embeddings can be ported onto microcontrollers.
